Liangliang Zhuang

ORCID: 0000-0003-4450-5513
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About
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Research Areas
  • Reliability and Maintenance Optimization
  • Probabilistic and Robust Engineering Design
  • Statistical Distribution Estimation and Applications
  • Machine Fault Diagnosis Techniques
  • Risk and Safety Analysis
  • Optimal Experimental Design Methods
  • Fault Detection and Control Systems
  • Advanced Sensor and Control Systems
  • Grey System Theory Applications
  • Advanced Algorithms and Applications
  • Environmental and Agricultural Sciences
  • Software Reliability and Analysis Research
  • Analysis of environmental and stochastic processes
  • Advanced Computational Techniques and Applications
  • Advanced Decision-Making Techniques

Zhejiang Gongshang University
2022-2024

Wenzhou University
2021

Xinjiang Agricultural University
2012

South China University of Technology
2011-2012

Traditionally, Gaussian assumption, implied by the Wiener process, is widely admitted for modeling degradation processes. However, when data exhibit heavy tails, this assumption not suitable. To overcome limitation, article proposes a novel class of tail-weighted multivariate model, which built upon Student-t process. The model able to account both between-unit variability and process dependency, while allows adjusting tail heaviness through tuning parameter degree freedom. For reliability...

10.1080/24725854.2024.2389538 article EN IISE Transactions 2024-08-07

Progressive-stress accelerated life testing (PSALT) is a special type of experiment that tests the lifetime product with continuously varying stress levels. Due to limitations equipments and costs, data collected by PSALT are usually censored have group effects. In order deal two characteristics in data, this paper presents novel model effects under progressive censoring. Two-stage Gauss-Hermite quadrature methods proposed estimate parameters, while interval estimates constructed bootstrap...

10.1080/16843703.2022.2147690 article EN Quality Technology & Quantitative Management 2022-12-01

In industry, many highly reliable products possess multiple performance characteristics (PCs) and they typically degrade simultaneously. When such PCs are governed by a common failure mechanism or influenced shared operating environmental condition, interdependence between these arises. To model dependence, this article proposes novel multivariate reparameterized inverse Gaussian (rIG) process model. It utilizes an additive structure; that is, the degradation of each marginal PC is...

10.1080/00224065.2024.2402850 article EN Journal of Quality Technology 2024-12-06
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